In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

import warnings
warnings.filterwarnings("ignore")
In [2]:
df_swiggy= pd.read_csv("Swiggy Bangalore Outlet Details.csv")
df_swiggy.head()
Out[2]:
Shop_Name Cuisine Location Rating Cost_for_Two
0 Kanti Sweets Sweets Koramangala, Koramangala 4.3 ₹ 150
1 Mumbai Tiffin North Indian, Home Food, Thalis, Combo Sector 5, HSR 4.4 ₹ 400
2 Sri Krishna sagar South Indian, North Indian, Fast Food, Beverag... 6th Block, Koramangala 4.1 ₹ 126
3 Al Daaz American, Arabian, Chinese, Desserts, Fast Foo... HSR, HSR 4.4 ₹ 400
4 Beijing Bites Chinese, Thai 5th Block, Koramangala 4.1 ₹ 450
In [3]:
# how many features are there in data sets
df_swiggy.columns
Out[3]:
Index(['Shop_Name', 'Cuisine', 'Location', 'Rating', 'Cost_for_Two'], dtype='object')
In [4]:
# check for missing values in dataset
df_swiggy.isnull().sum()
Out[4]:
Shop_Name       0
Cuisine         0
Location        0
Rating          0
Cost_for_Two    0
dtype: int64
In [5]:
df_swiggy.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 118 entries, 0 to 117
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Shop_Name     118 non-null    object
 1   Cuisine       118 non-null    object
 2   Location      118 non-null    object
 3   Rating        118 non-null    object
 4   Cost_for_Two  118 non-null    object
dtypes: object(5)
memory usage: 4.7+ KB
In [6]:
df_swiggy.describe(include="all") # element ,repeat,unqiue,how mant times
Out[6]:
Shop_Name Cuisine Location Rating Cost_for_Two
count 118 118 118 118 118
unique 115 79 65 13 30
top La Pino'z Pizza North Indian BTM, BTM 4.1 ₹ 300
freq 2 12 13 30 16
In [7]:
df_swiggy.duplicated().sum()
Out[7]:
0
In [8]:
df_swiggy["Rating"].unique()
Out[8]:
array(['4.3', '4.4', '4.1', '4.2', '3.9', '3.8', '4', '3.7', '3.6', '4.8',
       '4.5', '4.6', '--'], dtype=object)
In [9]:
# Replace  "--" rating with zero
df_swiggy["Rating"]=df_swiggy["Rating"].str.replace("--","0").astype(float)
In [10]:
df_swiggy["Rating"]
Out[10]:
0      4.3
1      4.4
2      4.1
3      4.4
4      4.1
      ... 
113    3.9
114    4.1
115    4.2
116    4.3
117    4.2
Name: Rating, Length: 118, dtype: float64
In [11]:
# How many unique entries for"Cost_for_Two" Future
df_swiggy["Cost_for_Two"].unique()
Out[11]:
array(['₹ 150', '₹ 400', '₹ 126', '₹ 450', '₹ 350', '₹ 200', '₹ 500',
       '₹ 247', '₹ 550', '₹ 300', '₹ 129', '₹ 250', '₹ 268', '₹ 600',
       '₹ 527', '₹ 130', '₹ 257', '₹ 280', '₹ 399', '₹ 220', '₹ 800',
       '₹ 100', '₹ 178', '₹ 120', '₹ 251', '₹ 650', '₹ 132', '₹ 153',
       '₹ 219', '₹ 193'], dtype=object)
In [12]:
df_swiggy["Cost_for_Two"]=df_swiggy["Cost_for_Two"].apply(lambda x:int(x.strip("₹  ")))
In [13]:
df_swiggy["Cost_for_Two"].dtype
Out[13]:
dtype('int64')
In [14]:
df_swiggy.head()
Out[14]:
Shop_Name Cuisine Location Rating Cost_for_Two
0 Kanti Sweets Sweets Koramangala, Koramangala 4.3 150
1 Mumbai Tiffin North Indian, Home Food, Thalis, Combo Sector 5, HSR 4.4 400
2 Sri Krishna sagar South Indian, North Indian, Fast Food, Beverag... 6th Block, Koramangala 4.1 126
3 Al Daaz American, Arabian, Chinese, Desserts, Fast Foo... HSR, HSR 4.4 400
4 Beijing Bites Chinese, Thai 5th Block, Koramangala 4.1 450
In [15]:
df_swiggy.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 118 entries, 0 to 117
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Shop_Name     118 non-null    object 
 1   Cuisine       118 non-null    object 
 2   Location      118 non-null    object 
 3   Rating        118 non-null    float64
 4   Cost_for_Two  118 non-null    int64  
dtypes: float64(1), int64(1), object(3)
memory usage: 4.7+ KB
In [16]:
df_swiggy.describe()
Out[16]:
Rating Cost_for_Two
count 118.000000 118.000000
mean 4.061864 321.008475
std 0.430845 137.286804
min 0.000000 100.000000
25% 4.000000 204.750000
50% 4.100000 300.000000
75% 4.300000 400.000000
max 4.800000 800.000000
In [17]:
# Distribution of Ratings":
df_valid_Ratings= df_swiggy[df_swiggy["Rating"]>0]
df_valid_Ratings
Out[17]:
Shop_Name Cuisine Location Rating Cost_for_Two
0 Kanti Sweets Sweets Koramangala, Koramangala 4.3 150
1 Mumbai Tiffin North Indian, Home Food, Thalis, Combo Sector 5, HSR 4.4 400
2 Sri Krishna sagar South Indian, North Indian, Fast Food, Beverag... 6th Block, Koramangala 4.1 126
3 Al Daaz American, Arabian, Chinese, Desserts, Fast Foo... HSR, HSR 4.4 400
4 Beijing Bites Chinese, Thai 5th Block, Koramangala 4.1 450
... ... ... ... ... ...
113 Wok Paper Scissors Pan-Asian, Chinese, Asian JNC Road, Koramangala 3.9 219
114 Savoury Restaurant Arabian, Middle Eastern, North Indian, Grill, ... Madiwala, BTM 4.1 600
115 Royal Treat North Indian, Chinese, Seafood, Biryani 5th block Koramangala, Koramangala 4.2 193
116 Thali 99 North Indian Koramangala, Koramangala 4.3 200
117 Mani's Dum Biryani Andhra, Biryani 1st Block, Koramangala 4.2 400

117 rows × 5 columns

In [18]:
# Distribution of  "Ratings":
sns.distplot(df_valid_Ratings["Rating"])
Out[18]:
<Axes: xlabel='Rating', ylabel='Density'>
No description has been provided for this image
In [19]:
# Handling Feature --> Location
df_swiggy["Location"].unique()
Out[19]:
array(['Koramangala, Koramangala', 'Sector 5, HSR',
       '6th Block, Koramangala', 'HSR, HSR', '5th Block, Koramangala',
       'Koramangala 4th  Block, Koramangala', 'BTM 2nd Stage, BTM',
       'BTM, BTM', '9th Main road, Koramangala', 'outer ring road, BTM',
       '7th Block, Koramangala', '1st MAin, Koramangala',
       'Bommanahalli, BTM', '6th block, Koramangala', 'Sector 4, HSR',
       'BTM 1st stage, BTM', 'Jakkasandra Extn, Koramangala',
       'Marutinagar Main Road, BTM', '1st Block, Koramangala',
       '4th Cross, BTM', 'koramangala, Koramangala', 'BTM 2nd stage, BTM',
       '3rd main, BTM', 'HSR 1st sector, HSR', 'Sector 7, HSR',
       '3rd Sector, HSR', 'Chocolate Factory Road, BTM',
       '16th Main Road, 2nd Stage, BTM', '1st Stage, BTM',
       'Hosur Main Road, Koramangala',
       '1st Cross Road, 5th Block, Near Jyothi Nivas College, Koramangala',
       'Mico Layout, BTM', '4th Cross, Koramangala',
       '4th Block, Koramangala', 'Intermediate Ring Road, Koramangala',
       '3rd sector, HSR', '8TH BLOCK, Koramangala',
       '4th b cross, Koramangala', 'SG palaya, BTM',
       "Venkatapura Main Rd, Teacher's Colony, Jakkasandra, HSR",
       'KHB Colony, Koramangala', 'Sector 3, HSR',
       'Bannerghatta Road, Jayanagar',
       '80 Feet Peripheral Road, Koramangala', 'Btm, BTM',
       'Near Wipro Park Signal, Koramangala', '16th Main Road, BTM',
       '2nd Stage, BTM', 'Kuvempu Nagar, Stage 2, BTM',
       'Koramangala 1st block, Koramangala',
       '5th Block Kormangala, Koramangala', 'Koramangla, Koramangala',
       '5th block, Koramangala', '9th Main Rd, Sector 6, HSR Layout, HSR',
       'Jay Bheema Nagar, BTM', 'Koramangala 6th block, Koramangala',
       'Maruthi Nagar, BTM', 'Sector 6, HSR',
       'Jakkasandra Village, Koramangala', '4th block, Koramangala',
       'Madiwala Junction, BTM', 'kormangala, Koramangala',
       'JNC Road, Koramangala', 'Madiwala, BTM',
       '5th block Koramangala, Koramangala'], dtype=object)
In [20]:
# Location that contains "koramangla"
swiggy_Koramangala= df_swiggy[df_swiggy["Location"].str.contains("Koramangala")]
swiggy_Koramangala
Out[20]:
Shop_Name Cuisine Location Rating Cost_for_Two
0 Kanti Sweets Sweets Koramangala, Koramangala 4.3 150
2 Sri Krishna sagar South Indian, North Indian, Fast Food, Beverag... 6th Block, Koramangala 4.1 126
4 Beijing Bites Chinese, Thai 5th Block, Koramangala 4.1 450
5 Kitchens of Punjab North Indian Koramangala 4th Block, Koramangala 4.2 350
9 Yumlane Pizza Pizzas, Italian, Mexican 9th Main road, Koramangala 3.8 150
... ... ... ... ... ...
112 Kritunga Andhra, Biryani 5th Block, Koramangala 3.9 500
113 Wok Paper Scissors Pan-Asian, Chinese, Asian JNC Road, Koramangala 3.9 219
115 Royal Treat North Indian, Chinese, Seafood, Biryani 5th block Koramangala, Koramangala 4.2 193
116 Thali 99 North Indian Koramangala, Koramangala 4.3 200
117 Mani's Dum Biryani Andhra, Biryani 1st Block, Koramangala 4.2 400

64 rows × 5 columns

In [21]:
swiggy_HSR=df_swiggy[df_swiggy["Location"].str.contains("HSR")]
swiggy_HSR
Out[21]:
Shop_Name Cuisine Location Rating Cost_for_Two
1 Mumbai Tiffin North Indian, Home Food, Thalis, Combo Sector 5, HSR 4.4 400
3 Al Daaz American, Arabian, Chinese, Desserts, Fast Foo... HSR, HSR 4.4 400
8 Hotel Manu South Indian, Kerala, Chinese, North Indian HSR, HSR 4.1 350
19 Shree Khana Khazana Indian, Rajasthani Sector 4, HSR 4.1 350
24 New Udupi Grand Chinese, Jain, North Indian, South Indian HSR, HSR 4.3 150
36 Biriyani Zone North Indian, Chinese, Biryani HSR 1st sector, HSR 4.1 600
37 Gongura's North Indian, Chinese, Biryani Sector 7, HSR 3.8 300
39 Leon Grill Turkish, Portuguese, American 3rd Sector, HSR 4.3 300
41 Cakewala Desserts HSR, HSR 4.3 450
57 Donne Biriyani House South Indian 3rd sector, HSR 4.0 300
58 Nanda's Andhra, Biryani HSR, HSR 4.0 400
61 Cake Garden Desserts, Bakery HSR, HSR 3.9 250
71 Nizams Biryani Biryani, Juices, Kebabs Venkatapura Main Rd, Teacher's Colony, Jakkasa... 3.6 200
73 Punjabi Rasoi North Indian Sector 3, HSR 4.0 800
98 Mandya Gowdru Donne Biryani Biryani HSR, HSR 0.0 350
99 Dindigul Thalapakatti Biriyani North Indian HSR, HSR 4.1 650
101 Easy Bites Snacks, American 9th Main Rd, Sector 6, HSR Layout, HSR 3.8 200
107 Junior Kuppanna Chettinad, South Indian Sector 6, HSR 4.0 550
In [22]:
# Locations that contains "BTM"
swiggy_BTM=df_swiggy[df_swiggy["Location"].str.contains("BTM")]
swiggy_BTM
Out[22]:
Shop_Name Cuisine Location Rating Cost_for_Two
6 99 VARIETY DOSA AND PAV BHAJI- Malli Mane Food... Fast Food, North Indian, Chinese BTM 2nd Stage, BTM 4.1 200
7 La Pino'z Pizza Italian BTM, BTM 3.9 500
10 Ambur Star Briyani Chinese, South Indian, North Indian, Desserts,... outer ring road, BTM 4.1 500
17 Sri Lakshmi Dhaba North Indian Bommanahalli, BTM 3.7 200
20 Just Bake - Cakes & confectioners Desserts, Bakery BTM 1st stage, BTM 4.3 300
22 Hotel Godavari North Indian, Chinese, Hyderabadi Marutinagar Main Road, BTM 4.0 400
25 Swad Punjab da Indian BTM, BTM 4.1 250
27 High N Hungry Andhra, Biryani, Chinese, Desserts, Fast Food,... 4th Cross, BTM 4.1 350
31 Bengali Fun Foods North Indian BTM 2nd stage, BTM 4.2 300
33 Oottupura Kerala, South Indian BTM, BTM 4.3 268
35 Hyderabadi Biryani Hub North Indian, Chinese, Biryani 3rd main, BTM 3.9 450
40 Venu's Donne Biryani Biryani Chocolate Factory Road, BTM 4.3 300
42 Swadista Aahar South Indian, Snacks, North Indian, Chinese 16th Main Road, 2nd Stage, BTM 4.1 250
44 Svadu Pure Ghee Sweets Desserts, Fast Food, Sweets, Chaat 1st Stage, BTM 4.1 200
45 Sai Abhiruchi Chinese, South Indian, Andhra, Hyderabadi BTM, BTM 3.7 250
49 Balaji's Veg North Indian, Chinese, South Indian Mico Layout, BTM 4.1 300
51 Donne Biryani Mandi Biryani, Andhra, South Indian BTM, BTM 4.0 150
60 calicut cafe restaurant Fast Food, Beverages BTM, BTM 4.1 280
65 World of asia Beverages, Chinese BTM, BTM 4.0 250
66 Ghar Ka Khana North Indian BTM, BTM 4.2 220
68 KANNUR FOOD POINT Kerala, Chinese SG palaya, BTM 3.9 300
69 KANNOOR RESTAURANT North Indian, Chinese BTM, BTM 4.0 250
70 Fattoush Arabian, Beverages, Biryani, Chinese, Desserts... BTM, BTM 3.9 400
76 BIRIYANI TASTE MASTH(BTM) North Indian, South Indian Btm, BTM 4.2 300
79 Tandoori Merchant Andhra, Biryani, Chinese, Desserts, Fast Food,... 4th Cross, BTM 4.2 100
80 Chinese Bae Chinese, Thai BTM, BTM 4.5 450
83 Abhiruchi Hotel Chinese, Hyderabadi, Biryani, Indian, South In... BTM, BTM 4.0 250
84 Punjabi Swag Punjabi, North Indian, Chinese, Fast Food, Hea... 16th Main Road, BTM 3.7 400
86 Gyaani Da Punjabi Dhaba North Indian 2nd Stage, BTM 4.0 500
87 Biriyani Bhatti Biryani, Hyderabadi, Andhra, North Indian, Sou... Kuvempu Nagar, Stage 2, BTM 4.1 350
92 BIRYANI CRAFTS Indian BTM, BTM 4.1 500
104 R.B Food Point Chinese, North Indian Jay Bheema Nagar, BTM 3.7 350
106 New Tasty Cafeteria Andhra, Chettinad, Chinese, Mughlai, North Indian Maruthi Nagar, BTM 4.0 350
110 Biryani Pot North Indian, Biryani Madiwala Junction, BTM 4.0 500
114 Savoury Restaurant Arabian, Middle Eastern, North Indian, Grill, ... Madiwala, BTM 4.1 600
In [23]:
sns.histplot(swiggy_BTM["Rating"], bins=10)   # bins = classes 0,2,4,4,6,
Out[23]:
<Axes: xlabel='Rating', ylabel='Count'>
No description has been provided for this image
In [24]:
sns.histplot(swiggy_BTM["Cost_for_Two"],bins=10)
Out[24]:
<Axes: xlabel='Cost_for_Two', ylabel='Count'>
No description has been provided for this image
In [25]:
 # conclusion : 
# BTM: Most has 4.0 to 4.2 Rating and Approx. cost for Two peoples lies between 200 to 350
In [26]:
sns.histplot(swiggy_HSR["Rating"], bins=10)
Out[26]:
<Axes: xlabel='Rating', ylabel='Count'>
No description has been provided for this image
In [27]:
sns.histplot(swiggy_HSR["Cost_for_Two"],bins=10)
Out[27]:
<Axes: xlabel='Cost_for_Two', ylabel='Count'>
No description has been provided for this image
In [28]:
sns.histplot(swiggy_Koramangala["Rating"],bins=10)
Out[28]:
<Axes: xlabel='Rating', ylabel='Count'>
No description has been provided for this image
In [29]:
sns.histplot(swiggy_Koramangala["Cost_for_Two"],bins=15)
Out[29]:
<Axes: xlabel='Cost_for_Two', ylabel='Count'>
No description has been provided for this image
In [30]:
# conclusion:
# Koramangala: Most has 4.0 to 4.3 Rating and Approx. Cost for Two people lies between 200 to 350.(max.Cost goes upto 600)
In [31]:
# Analysis "Approx Cost of 2 people " vs "Rating". Find out the relationship betwwen them.
df_Highest_Rated_Restaurants= df_swiggy[df_swiggy["Rating"]>= 4.0]
df_Highest_Rated_Restaurants
Out[31]:
Shop_Name Cuisine Location Rating Cost_for_Two
0 Kanti Sweets Sweets Koramangala, Koramangala 4.3 150
1 Mumbai Tiffin North Indian, Home Food, Thalis, Combo Sector 5, HSR 4.4 400
2 Sri Krishna sagar South Indian, North Indian, Fast Food, Beverag... 6th Block, Koramangala 4.1 126
3 Al Daaz American, Arabian, Chinese, Desserts, Fast Foo... HSR, HSR 4.4 400
4 Beijing Bites Chinese, Thai 5th Block, Koramangala 4.1 450
... ... ... ... ... ...
111 Bowl 99 North Indian, South Indian kormangala, Koramangala 4.4 200
114 Savoury Restaurant Arabian, Middle Eastern, North Indian, Grill, ... Madiwala, BTM 4.1 600
115 Royal Treat North Indian, Chinese, Seafood, Biryani 5th block Koramangala, Koramangala 4.2 193
116 Thali 99 North Indian Koramangala, Koramangala 4.3 200
117 Mani's Dum Biryani Andhra, Biryani 1st Block, Koramangala 4.2 400

92 rows × 5 columns

In [32]:
df_Highest_Rated_Restaurants=df_Highest_Rated_Restaurants.loc[:, ["Shop_Name","Rating","Cost_for_Two"]]
df_Highest_Rated_Restaurants
Out[32]:
Shop_Name Rating Cost_for_Two
0 Kanti Sweets 4.3 150
1 Mumbai Tiffin 4.4 400
2 Sri Krishna sagar 4.1 126
3 Al Daaz 4.4 400
4 Beijing Bites 4.1 450
... ... ... ...
111 Bowl 99 4.4 200
114 Savoury Restaurant 4.1 600
115 Royal Treat 4.2 193
116 Thali 99 4.3 200
117 Mani's Dum Biryani 4.2 400

92 rows × 3 columns

In [33]:
df_Highest_Rated_Restaurants=df_Highest_Rated_Restaurants.groupby(["Shop_Name","Rating"])["Cost_for_Two"].agg("mean")  # series
df_Highest_Rated_Restaurants
Out[33]:
Shop_Name                                             Rating
99 VARIETY DOSA AND JUICE-Malli mane food court       4.1       100.0
99 VARIETY DOSA AND PAV BHAJI- Malli Mane Food Court  4.1       200.0
A2B - Adyar Ananda Bhavan                             4.2       450.0
Abhiruchi Hotel                                       4.0       250.0
Al Daaz                                               4.4       400.0
                                                                ...  
Venu's Donne Biryani                                  4.3       300.0
WarmOven Cake & Desserts                              4.1       200.0
World of asia                                         4.0       250.0
XO Belgian Waffle                                     4.3       250.0
calicut cafe restaurant                               4.1       280.0
Name: Cost_for_Two, Length: 91, dtype: float64
In [34]:
df_Highest_Rated_Restaurants=df_Highest_Rated_Restaurants.reset_index()     # data frame
df_Highest_Rated_Restaurants
Out[34]:
Shop_Name Rating Cost_for_Two
0 99 VARIETY DOSA AND JUICE-Malli mane food court 4.1 100.0
1 99 VARIETY DOSA AND PAV BHAJI- Malli Mane Food... 4.1 200.0
2 A2B - Adyar Ananda Bhavan 4.2 450.0
3 Abhiruchi Hotel 4.0 250.0
4 Al Daaz 4.4 400.0
... ... ... ...
86 Venu's Donne Biryani 4.3 300.0
87 WarmOven Cake & Desserts 4.1 200.0
88 World of asia 4.0 250.0
89 XO Belgian Waffle 4.3 250.0
90 calicut cafe restaurant 4.1 280.0

91 rows × 3 columns

In [35]:
import plotly.express as px
fig=px.scatter(
    x=df_Highest_Rated_Restaurants["Cost_for_Two"],
    y=df_Highest_Rated_Restaurants["Rating"],
    color=df_Highest_Rated_Restaurants["Rating"],
    size=df_Highest_Rated_Restaurants["Cost_for_Two"],
    labels={
        "x": "Approx. Cost_for_Two",
        "y": "Rating",
        "color":"Rating_Indicator"})

fig.update_layout(
    template="plotly_dark",
    title="Analysis 'Approx cost of2 people' vs 'rating'")
fig.show()
In [36]:
##Q Analysis Affodarble\Budgeted and Higest Rated Restaurants  of Banglore:
df_Affordable_Restaurants=df_swiggy[(df_swiggy["Cost_for_Two"] <=500) & (df_swiggy["Rating"] >=4.0)]
df_Affordable_Restaurants
Out[36]:
Shop_Name Cuisine Location Rating Cost_for_Two
0 Kanti Sweets Sweets Koramangala, Koramangala 4.3 150
1 Mumbai Tiffin North Indian, Home Food, Thalis, Combo Sector 5, HSR 4.4 400
2 Sri Krishna sagar South Indian, North Indian, Fast Food, Beverag... 6th Block, Koramangala 4.1 126
3 Al Daaz American, Arabian, Chinese, Desserts, Fast Foo... HSR, HSR 4.4 400
4 Beijing Bites Chinese, Thai 5th Block, Koramangala 4.1 450
... ... ... ... ... ...
110 Biryani Pot North Indian, Biryani Madiwala Junction, BTM 4.0 500
111 Bowl 99 North Indian, South Indian kormangala, Koramangala 4.4 200
115 Royal Treat North Indian, Chinese, Seafood, Biryani 5th block Koramangala, Koramangala 4.2 193
116 Thali 99 North Indian Koramangala, Koramangala 4.3 200
117 Mani's Dum Biryani Andhra, Biryani 1st Block, Koramangala 4.2 400

82 rows × 5 columns

In [37]:
df_Affordable_Restaurants=df_Affordable_Restaurants.groupby(["Shop_Name", "Rating"])["Cost_for_Two"].agg("mean")
df_Affordable_Restaurants=df_Affordable_Restaurants.reset_index()
df_Affordable_Restaurants
Out[37]:
Shop_Name Rating Cost_for_Two
0 99 VARIETY DOSA AND JUICE-Malli mane food court 4.1 100.0
1 99 VARIETY DOSA AND PAV BHAJI- Malli Mane Food... 4.1 200.0
2 A2B - Adyar Ananda Bhavan 4.2 450.0
3 Abhiruchi Hotel 4.0 250.0
4 Al Daaz 4.4 400.0
... ... ... ...
76 Venu's Donne Biryani 4.3 300.0
77 WarmOven Cake & Desserts 4.1 200.0
78 World of asia 4.0 250.0
79 XO Belgian Waffle 4.3 250.0
80 calicut cafe restaurant 4.1 280.0

81 rows × 3 columns

In [38]:
df_Affordable_Restaurants.sort_values(by=["Rating"],ascending=False, inplace=True)
df_Affordable_Restaurants
Out[38]:
Shop_Name Rating Cost_for_Two
41 Khichdi Experiment 4.8 200.0
54 Natural Ice Cream 4.6 150.0
21 Corner House Ice Cream 4.6 250.0
20 Chinese Bae 4.5 450.0
50 Mumbai Tiffin 4.4 400.0
... ... ... ...
55 New Tasty Cafeteria 4.0 350.0
53 Nandhana Palace 4.0 500.0
52 Nanda's 4.0 400.0
45 Maa Di Hatti 4.0 129.0
29 Gyaani Da Punjabi Dhaba 4.0 500.0

81 rows × 3 columns

In [39]:
plt.figure(figsize=(18,7))
sns.barplot(
    x=df_Affordable_Restaurants["Shop_Name"],
    y=df_Affordable_Restaurants["Cost_for_Two"],
    data=df_Affordable_Restaurants)
Out[39]:
<Axes: xlabel='Shop_Name', ylabel='Cost_for_Two'>
No description has been provided for this image
In [40]:
plt.figure(figsize=(18,7))
sns.barplot(
    x=df_Affordable_Restaurants["Shop_Name"],
    y=df_Affordable_Restaurants["Cost_for_Two"],
    data=df_Affordable_Restaurants)

plt.title(
    "Affodarble\Budgeted and Higest Rated Restaurants (Banglore)",
    fontsize=14,
    fontweight="bold",
    fontstyle="italic"
)

plt.xlabel("Shop_Name", fontsize=10, fontweight="bold")
plt.ylabel("Approx. Cost_for_Two", fontsize=10, fontweight="bold")
plt.xticks(rotation=90)
plt.show()
No description has been provided for this image
In [41]:
#Q Top 15 cheapest & Highest Rated Restaurants with Approx. Cost for 2 people:
df_chepest_Restaurants=df_Affordable_Restaurants.sort_values(by="Cost_for_Two", ascending=True)
df_chepest_Restaurants
Out[41]:
Shop_Name Rating Cost_for_Two
72 Tandoori Merchant 4.2 100.0
0 99 VARIETY DOSA AND JUICE-Malli mane food court 4.1 100.0
51 NIC Natural Ice Creams 4.2 120.0
68 Sri Krishna sagar 4.1 126.0
45 Maa Di Hatti 4.0 129.0
... ... ... ...
5 Ambur Star Briyani 4.1 500.0
7 BIRYANI CRAFTS 4.1 500.0
53 Nandhana Palace 4.0 500.0
46 Madeena Hotel 4.1 500.0
29 Gyaani Da Punjabi Dhaba 4.0 500.0

81 rows × 3 columns

In [42]:
fig=px.bar(
    data_frame=df_chepest_Restaurants,
    x=df_chepest_Restaurants["Shop_Name"][0:15],
    y=df_chepest_Restaurants["Cost_for_Two"][0:15],
    color=df_chepest_Restaurants["Rating"][0:15],
    labels={
        "x": "Restaurant_Name",
        "y": "Approx. Cost_for_Two (₹ )",
        "color": "Rating",})

fig.update_layout(
    template="plotly_dark",
    title="Top 15 Cheapest & Highesty Rated Restaurants with Approx. Cost for 2 People")
fig.show()
In [43]:
fig=px.scatter(
    data_frame=df_chepest_Restaurants,
    x=df_chepest_Restaurants["Shop_Name"][0:15],
    y=df_chepest_Restaurants["Cost_for_Two"][0:15],
    color=df_chepest_Restaurants["Rating"][0:15],
    labels={
        "x": "Restaurant_Name",
        "y": "Approx. Cost_for_Two (₹ )",
        "color": "Rating",})

fig.update_layout(
    template="plotly_dark",
    title="Top 15 Cheapest & Highesty Rated Restaurants with Approx. Cost for 2 People")
fig.show()
In [44]:
# Q Top 15 expensive & Highest Rated Restaurants with Approx. Cost For 2 People:
df_Expensive_Restaurants=df_Highest_Rated_Restaurants.sort_values(
    by="Cost_for_Two", ascending=False)
df_Expensive_Restaurants
Out[44]:
Shop_Name Rating Cost_for_Two
67 Punjabi Rasoi 4.0 800.0
26 Dindigul Thalapakatti Biriyani 4.1 650.0
73 Savoury Restaurant 4.1 600.0
81 Taco Bell 4.3 600.0
66 Pizza Hut 4.0 600.0
... ... ... ...
49 Maa Di Hatti 4.0 129.0
77 Sri Krishna sagar 4.1 126.0
56 NIC Natural Ice Creams 4.2 120.0
82 Tandoori Merchant 4.2 100.0
0 99 VARIETY DOSA AND JUICE-Malli mane food court 4.1 100.0

91 rows × 3 columns

In [45]:
fig=px.bar(
    data_frame=df_Expensive_Restaurants,
    x=df_Expensive_Restaurants["Shop_Name"][0:15],
    y=df_Expensive_Restaurants["Cost_for_Two"][0:15],
    color=df_Expensive_Restaurants["Rating"][0:15],
    labels={
        "x": "Restaurant_Name",
        "y": "Approx. Cost_for_Two (₹ )",
        "color": "Rating",})

fig.update_layout(
    template="plotly_dark",
    title="Top 15 Expensive & Highesty Rated Restaurants with Approx. Cost for 2 People")
fig.show()
In [46]:
fig=px.scatter(
    data_frame=df_Expensive_Restaurants,
    x=df_Expensive_Restaurants["Shop_Name"][0:15],
    y=df_Expensive_Restaurants["Cost_for_Two"][0:15],
    color=df_Expensive_Restaurants["Rating"][0:15],
    labels={
        "x": "Restaurant_Name",
        "y": "Approx. Cost_for_Two (₹ )",
        "color": "Rating",})

fig.update_layout(
    template="plotly_dark",
    title="Top 15 Expensive & Highesty Rated Restaurants with Approx. Cost for 2 People")
fig.show()
In [47]:
  # CUISINE ANALYSIS
df_swiggy["Cuisine"]= df_swiggy["Cuisine"].str.title()
df_swiggy["Cuisine"]
Out[47]:
0                                                 Sweets
1                 North Indian, Home Food, Thalis, Combo
2      South Indian, North Indian, Fast Food, Beverag...
3      American, Arabian, Chinese, Desserts, Fast Foo...
4                                          Chinese, Thai
                             ...                        
113                            Pan-Asian, Chinese, Asian
114    Arabian, Middle Eastern, North Indian, Grill, ...
115              North Indian, Chinese, Seafood, Biryani
116                                         North Indian
117                                      Andhra, Biryani
Name: Cuisine, Length: 118, dtype: object
In [48]:
df_swiggy["Cuisine"].unique()
Out[48]:
array(['Sweets', 'North Indian, Home Food, Thalis, Combo',
       'South Indian, North Indian, Fast Food, Beverages, Jain',
       'American, Arabian, Chinese, Desserts, Fast Food, Mughlai, North Indian',
       'Chinese, Thai', 'North Indian',
       'Fast Food, North Indian, Chinese', 'Italian',
       'South Indian, Kerala, Chinese, North Indian',
       'Pizzas, Italian, Mexican',
       'Chinese, South Indian, North Indian, Desserts, Fast Food, Kerala, Andhra, Beverages, Mughlai, Seafood',
       'Desserts', 'Chinese, Andhra, Biryani, Seafood', 'Chinese',
       'South Indian, Chinese, Desserts, North Indian',
       'Arabian, Fast Food', 'Desserts, Beverages', 'Indian, Rajasthani',
       'Desserts, Bakery', 'Chinese, Healthy Food, North Indian',
       'North Indian, Chinese, Hyderabadi', 'Fast Food',
       'Chinese, Jain, North Indian, South Indian', 'Indian',
       'North Indian, South Indian, Chinese',
       'Andhra, Biryani, Chinese, Desserts, Fast Food, Seafood, South Indian',
       'American, Fast Food',
       'Biryani, Seafood, North Indian, Chinese, Desserts, Andhra, South Indian',
       'Snacks, American', 'South Indian', 'Kerala, South Indian',
       'Mexican', 'North Indian, Chinese, Biryani',
       'Turkish, Portuguese, American', 'Biryani',
       'South Indian, Snacks, North Indian, Chinese',
       'Desserts, Fast Food, Sweets, Chaat',
       'Chinese, South Indian, Andhra, Hyderabadi', 'Pizzas, Fast Food',
       'Biryani, Mughlai, South Indian', 'Chinese, Asian',
       'North Indian, Chinese, South Indian', 'Italian, Desserts, Pizzas',
       'Biryani, Andhra, South Indian',
       'Chinese, Continental, Italian, Mediterranean, Thai, Lebanese, American, Asian, Beverages, Bakery, Biryani, Cafe, Desserts, Healthy Food, Mexican, North Indian, Salads, Pizzas',
       'Pizzas, Chinese, Pastas, Salads, American, Continental',
       'Andhra, Biryani',
       'Chinese, South Indian, North Indian, Fast Food',
       'Fast Food, Beverages',
       'Biryani, South Indian, North Indian, Fast Food, Andhra, Beverages, Mughlai, Seafood, Punjabi, Hyderabadi, Chinese',
       'Beverages, Chinese',
       'South Indian, Biryani, Kerala, North Indian, Chinese',
       'Kerala, Chinese', 'North Indian, Chinese',
       'Arabian, Beverages, Biryani, Chinese, Desserts, North Indian',
       'Biryani, Juices, Kebabs', 'Andhra, South Indian',
       'Beverages, Cafe, Snacks', 'North Indian, South Indian',
       'Turkish, Portuguese, American, Grill',
       'Home Food, Healthy Food, Indian', 'Ice Cream',
       'Chinese, Hyderabadi, Biryani, Indian, South Indian, Andhra, Tandoor',
       'Punjabi, North Indian, Chinese, Fast Food, Healthy Food, Mughlai, Desserts',
       'American',
       'Biryani, Hyderabadi, Andhra, North Indian, South Indian',
       'Fast Food, Juices, North Indian',
       'North Indian, Chaat, Snacks, Fast Food',
       'Desserts, Mughlai, Seafood', 'Ice Cream, Desserts',
       'Chinese, North Indian', 'Biryani, Kebabs',
       'Andhra, Chettinad, Chinese, Mughlai, North Indian',
       'Chettinad, South Indian',
       'Continental, Indian, Pan-Asian, Oriental',
       'North Indian, Biryani', 'Pan-Asian, Chinese, Asian',
       'Arabian, Middle Eastern, North Indian, Grill, Seafood, Kerala, Chinese',
       'North Indian, Chinese, Seafood, Biryani'], dtype=object)
In [49]:
# lstrip is removed the space 

freq_dict={}
for i in df_swiggy["Cuisine"].unique():
    Cuisines_lists=i.split(",")
    for Cuisine in Cuisines_lists:
        Cuisine=Cuisine.lstrip(" ")
        if Cuisine in freq_dict:
            freq_dict[Cuisine]= freq_dict[Cuisine] + 1
        else:
            freq_dict[Cuisine]= 1

print(freq_dict)
print()
print("Total Records: \t", len(freq_dict))
            
        
        

{'Sweets': 2, 'North Indian': 32, 'Home Food': 2, 'Thalis': 1, 'Combo': 1, 'South Indian': 23, 'Fast Food': 16, 'Beverages': 9, 'Jain': 2, 'American': 8, 'Arabian': 4, 'Chinese': 35, 'Desserts': 15, 'Mughlai': 7, 'Thai': 2, 'Italian': 4, 'Kerala': 6, 'Pizzas': 5, 'Mexican': 3, 'Andhra': 12, 'Seafood': 8, 'Biryani': 18, 'Indian': 5, 'Rajasthani': 1, 'Bakery': 2, 'Healthy Food': 4, 'Hyderabadi': 5, 'Snacks': 4, 'Turkish': 2, 'Portuguese': 2, 'Chaat': 2, 'Asian': 3, 'Continental': 3, 'Mediterranean': 1, 'Lebanese': 1, 'Cafe': 2, 'Salads': 2, 'Pastas': 1, 'Punjabi': 2, 'Juices': 2, 'Kebabs': 2, 'Grill': 2, 'Ice Cream': 2, 'Tandoor': 1, 'Chettinad': 2, 'Pan-Asian': 2, 'Oriental': 1, 'Middle Eastern': 1}

Total Records: 	 48
In [50]:
# Extracting   Cuisine name and there frequency
Cuisine = freq_dict.keys()
freq= freq_dict.values()

df_Cuisine_Analysis= pd.DataFrame()

# Creating a dataframe having two features-----> Cuisine and count
df_Cuisine_Analysis["Cuisine"]= Cuisine
df_Cuisine_Analysis["Count"]= freq

df_Cuisine_Analysis
Out[50]:
Cuisine Count
0 Sweets 2
1 North Indian 32
2 Home Food 2
3 Thalis 1
4 Combo 1
5 South Indian 23
6 Fast Food 16
7 Beverages 9
8 Jain 2
9 American 8
10 Arabian 4
11 Chinese 35
12 Desserts 15
13 Mughlai 7
14 Thai 2
15 Italian 4
16 Kerala 6
17 Pizzas 5
18 Mexican 3
19 Andhra 12
20 Seafood 8
21 Biryani 18
22 Indian 5
23 Rajasthani 1
24 Bakery 2
25 Healthy Food 4
26 Hyderabadi 5
27 Snacks 4
28 Turkish 2
29 Portuguese 2
30 Chaat 2
31 Asian 3
32 Continental 3
33 Mediterranean 1
34 Lebanese 1
35 Cafe 2
36 Salads 2
37 Pastas 1
38 Punjabi 2
39 Juices 2
40 Kebabs 2
41 Grill 2
42 Ice Cream 2
43 Tandoor 1
44 Chettinad 2
45 Pan-Asian 2
46 Oriental 1
47 Middle Eastern 1
In [51]:
plt.figure(figsize=(20,8))
sns.barplot(
    x=df_Cuisine_Analysis["Cuisine"],
    y=df_Cuisine_Analysis["Count"],
    data=df_Cuisine_Analysis,)

plt.xticks(rotation=90)
plt.title(
    "Cuisine Overall Analysis (Banglore)",
    fontsize=14,
    fontweight="bold",
    fontstyle="italic",)
plt.xlabel("Cuisine", fontsize=11, fontweight="bold")
plt.ylabel("Number of Restaurants", fontsize=11, fontweight="bold")
plt.show()
No description has been provided for this image
In [52]:
fig=px.pie(
    data_frame=df_Cuisine_Analysis,
    names=df_Cuisine_Analysis["Cuisine"][:10],
    values=df_Cuisine_Analysis["Count"][:10],
    title="Distribution of Cuisine in Banglore Restaurants",width=700,height=700)
# fig.update_traces(textpostition"inside",textinfo="percent+label(")
fig.show()
In [53]:
freq_BTM={}
for i in swiggy_BTM["Cuisine"].unique():
    Cuisines_lists=i.split(",")
    for Cuisine in Cuisines_lists:
        Cuisine=Cuisine.lstrip(" ")
        if Cuisine in freq_BTM:
            freq_BTM[Cuisine]= freq_BTM[Cuisine] + 1
        else:
            freq_BTM[Cuisine]= 1

print(freq_BTM)
print()
print("Total Records: \t", len(freq_BTM))
            
        
        

{'Fast Food': 6, 'North Indian': 16, 'Chinese': 18, 'Italian': 1, 'South Indian': 10, 'Desserts': 6, 'Kerala': 4, 'Andhra': 7, 'Beverages': 4, 'Mughlai': 3, 'Seafood': 3, 'Bakery': 1, 'Hyderabadi': 4, 'Indian': 2, 'Biryani': 8, 'Snacks': 1, 'Sweets': 1, 'Chaat': 1, 'Arabian': 2, 'Thai': 1, 'Tandoor': 1, 'Punjabi': 1, 'Healthy Food': 1, 'Chettinad': 1, 'Middle Eastern': 1, 'Grill': 1}

Total Records: 	 26
In [54]:
freq_BTM.keys()
Out[54]:
dict_keys(['Fast Food', 'North Indian', 'Chinese', 'Italian', 'South Indian', 'Desserts', 'Kerala', 'Andhra', 'Beverages', 'Mughlai', 'Seafood', 'Bakery', 'Hyderabadi', 'Indian', 'Biryani', 'Snacks', 'Sweets', 'Chaat', 'Arabian', 'Thai', 'Tandoor', 'Punjabi', 'Healthy Food', 'Chettinad', 'Middle Eastern', 'Grill'])
In [55]:
Cusisne=freq_BTM.keys()
freq=freq_BTM.values()

dict_BTM={"Cuisine": Cusisne, "Count": freq}

df_Cuisine_BTM=pd.DataFrame(dict_BTM)
df_Cuisine_BTM.head()
Out[55]:
Cuisine Count
0 Fast Food 6
1 North Indian 16
2 Chinese 18
3 Italian 1
4 South Indian 10
In [56]:
plt.figure(figsize=(20,8))
sns.barplot(
    x=df_Cuisine_BTM["Cuisine"],y=df_Cuisine_BTM["Count"],data=df_Cuisine_BTM)

plt.xticks(rotation=90)

plt.title(
    "Cuisine Overall BTM (Banglore)",
    fontsize=14,
    fontweight="bold",
    fontstyle="italic",)
plt.xlabel("Cuisine", fontsize=11, fontweight="bold")
plt.ylabel("Number of Restaurants", fontsize=11,fontweight="bold")
plt.show()
No description has been provided for this image
In [57]:
freq_HSR={}
for i in swiggy_HSR["Cuisine"].unique():
    Cuisines_lists=i.split(",")
    for Cuisine in Cuisines_lists:
        Cuisine=Cuisine.lstrip(" ")
        if Cuisine in freq_HSR:
            freq_HSR[Cuisine]= freq_HSR[Cuisine] + 1
        else:
            freq_HSR[Cuisine]= 1

print(freq_HSR)
print()
print("Total Records: \t", len(freq_HSR))

{'North Indian': 6, 'Home Food': 1, 'Thalis': 1, 'Combo': 1, 'American': 3, 'Arabian': 1, 'Chinese': 4, 'Desserts': 3, 'Fast Food': 1, 'Mughlai': 1, 'South Indian': 4, 'Kerala': 1, 'Indian': 1, 'Rajasthani': 1, 'Jain': 1, 'Biryani': 4, 'Turkish': 1, 'Portuguese': 1, 'Andhra': 1, 'Bakery': 1, 'Juices': 1, 'Kebabs': 1, 'Snacks': 1, 'Chettinad': 1}

Total Records: 	 24
In [58]:
Cuisine=freq_HSR.keys()
freq=freq_HSR.values()

dict_HSR={"Cuisine": Cuisine, "Count": freq}

df_Cuisine_HSR=pd.DataFrame(dict_HSR)
df_Cuisine_HSR.head()
Out[58]:
Cuisine Count
0 North Indian 6
1 Home Food 1
2 Thalis 1
3 Combo 1
4 American 3
In [59]:
plt.figure(figsize=(20,8))
sns.barplot(
    x=df_Cuisine_HSR["Cuisine"],y=df_Cuisine_HSR["Count"],data=df_Cuisine_HSR)

plt.xticks(rotation=90)

plt.title(
    "Cuisine Overall HSR (Banglore)",
    fontsize=14,
    fontweight="bold",
    fontstyle="italic",)
plt.xlabel("Cuisine", fontsize=11, fontweight="bold")
plt.ylabel("Number of Restaurants", fontsize=11,fontweight="bold")
plt.show()
No description has been provided for this image
In [60]:
fig=px.pie(
    data_frame=df_Cuisine_HSR,
    names=df_Cuisine_HSR["Cuisine"][:10],
    values=df_Cuisine_HSR["Count"][:10],
    title="Distribution of Cuisine in Banglore Restaurants",width=700,height=700)
# fig.update_traces(textpostition"inside",textinfo="percent+label(")
fig.show()
In [61]:
freq_koramangala={}
for i in swiggy_Koramangala["Cuisine"].unique():
    Cuisines_lists=i.split(",")
    for Cuisine in Cuisines_lists:
        Cuisine=Cuisine.lstrip(" ")
        if Cuisine in freq_koramangala:
            freq_koramangala[Cuisine]= freq_koramangala[Cuisine] + 1
        else:
            freq_koramangala[Cuisine]= 1

print(freq_koramangala)
print()
print("Total Records: \t", len(freq_koramangala))

{'Sweets': 1, 'South Indian': 11, 'North Indian': 14, 'Fast Food': 9, 'Beverages': 5, 'Jain': 1, 'Chinese': 15, 'Thai': 2, 'Pizzas': 5, 'Italian': 4, 'Mexican': 3, 'Desserts': 8, 'Andhra': 5, 'Biryani': 10, 'Seafood': 5, 'Arabian': 1, 'Healthy Food': 3, 'American': 6, 'Snacks': 3, 'Mughlai': 3, 'Asian': 3, 'Continental': 3, 'Mediterranean': 1, 'Lebanese': 1, 'Bakery': 1, 'Cafe': 2, 'Salads': 2, 'Pastas': 1, 'Punjabi': 1, 'Hyderabadi': 1, 'Kerala': 1, 'Turkish': 1, 'Portuguese': 1, 'Grill': 1, 'Home Food': 1, 'Indian': 2, 'Ice Cream': 2, 'Juices': 1, 'Chaat': 1, 'Kebabs': 1, 'Pan-Asian': 2, 'Oriental': 1}

Total Records: 	 42
In [124]:
Cuisine=freq_koramangala.keys()
freq=freq_koramangala.values()

dict_koramangala={"Cuisine": Cuisine, "Count": freq}

df_Cuisine_koramangala=pd.DataFrame(dict_HSR)
df_Cuisine_koramangala.head()
Out[124]:
Cuisine Count
0 North Indian 6
1 Home Food 1
2 Thalis 1
3 Combo 1
4 American 3
In [ ]: